Our model is based on the DDIM implementation available in [1]} (under the MIT license). We trained our model on two image datasets (i) CelebA 64x64 [2]} and (ii) LSUN Church 256x256 [3]}. The Fréchet Inception Distance (FID) [4]} is used as the benchmark metric. For all experiments, similarly to previous work [5]}, we compute the FID score with \(50,000\)  generated images, using the torch-fidelity implementation [6]}. Similar to [5]}, the training noise schedule \(\beta _1, ... ,\beta _T\)  is linear with values raging from \(0.0001\)  to \(0.02\) . For other hyperparameters (e.g. learning rate, batch size etc) we use the same parameters that appear in DDPM [8]}. We use eight Nvidia Volta V100 GPUs to train our models. The \(\theta _0\)  parameter for Gamma distribution set to \(0.001\) .
